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Improvement of the prediction accuracy and efficiency of hot strength of austenitic steels with optimised ANN training schemes
journal contribution
posted on 1998-08-01, 00:00 authored by B Wang, Lingxue KongLingxue Kong, Peter HodgsonPeter Hodgson, D C CollinsonThe hot strength of austenitic steels of different carbon contents was modelled using an artificial neural network (ANN) model with optimum training data. As training data employed in a traditional neural network model were randomly selected from experimental data, they were not representative and the prediction accuracy and efficiency were therefore significantly affected. In this work, only representatively experimental data were used for training and during the procedure, one tenth of the training data extracted from experiment were used for testing the training model and terminating the modelling. The effects of the carbon con tent on flow stress, peak strains and peak stresses observed from the experiment for both training and test data were accurately represented with the ANN scheme reported in this work.
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Journal
Metals and materials internationalVolume
4Issue
4Pagination
823 - 826Publisher
Springer VerlagLocation
Berlin, GermanyPublisher DOI
ISSN
1598-9623Language
engPublication classification
C1.1 Refereed article in a scholarly journalCopyright notice
1998, Springer VerlagUsage metrics
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